It’s like having a translator between you and your computer!
So how exactly does this magic happen? Well, let me give you an example. Let’s say we want to build a model that can recognize cats from dogs in pictures. Instead of writing out all the fancy math equations and code for each layer (which would take forever), Keras lets us do it like this:
# Importing necessary modules from Keras library
from keras.models import Sequential # Importing the Sequential model, which allows us to add layers one by one
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense # Importing the Conv2D, MaxPooling2D, Flatten, and Dense layers, which are building blocks for our model
# Creating a Sequential model
model = Sequential() # Initializing the Sequential model and assigning it to the variable "model"
# Adding a Convolutional layer
model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3))) # Adding a Conv2D layer with 32 filters, a kernel size of 3x3, and an input shape of 64x64x3 (64x64 pixels with 3 color channels)
# Adding a MaxPooling layer
model.add(MaxPooling2D((2, 2))) # Adding a MaxPooling2D layer with a pool size of 2x2, which reduces the size of the feature map by half
# Adding a Flatten layer
model.add(Flatten()) # Adding a Flatten layer, which converts the 2D feature map into a 1D vector
# Adding a Dense layer
model.add(Dense(10)) # Adding a Dense layer with 10 neurons, which will be used for classification
# The model is now ready to be trained and used for recognizing cats and dogs in pictures!
That’s it! We just created a simple model with four layers: Convolutional (for detecting edges and shapes), Max Pooling (to reduce the size of our input data without losing important features), Flattening (to convert our 2D images into a single vector that can be fed into the next layer), and Dense (which is essentially just a fancy name for a fully connected neural network).
Now, let’s say we want to add some more layers or change the number of filters in one of them. Instead of having to rewrite all our code from scratch, Keras lets us do it like this:
# Import necessary modules from Keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
# Create a sequential model
model = Sequential()
# Add a convolutional layer with 32 filters, kernel size of (3, 3), and input shape of (64, 64, 3)
model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3)))
# Add a new convolutional layer with 64 filters, kernel size of (5, 5), and ReLU activation function
model.add(Conv2D(64, (5, 5), activation='relu'))
# Add a max pooling layer with pool size of (2, 2)
model.add(MaxPooling2D((2, 2)))
# Add a flatten layer to convert the output of the previous layer into a 1-dimensional vector
model.add(Flatten())
# Add a fully connected layer with 100 neurons as the output layer
model.add(Dense(100))
# The purpose of each code segment is as follows:
# - Import necessary modules from Keras to use in the script
# - Create a sequential model to add layers to
# - Add a convolutional layer to extract features from the input image
# - Add a new convolutional layer to extract more complex features
# - Add a max pooling layer to reduce the spatial size of the output
# - Add a flatten layer to convert the output into a 1-dimensional vector
# - Add a fully connected layer as the output layer to make predictions based on the extracted features
See how easy that was? No more writing out all those complicated math equations and code for each layer! Keras takes care of all that for us, so we can focus on what really matters: building awesome models that work!